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Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
1.6 seconds
Metric details
|
score |
threshold |
| logloss |
1.28771 |
nan |
| auc |
0.495056 |
nan |
| f1 |
0.648649 |
1.02425e-22 |
| accuracy |
0.536 |
0.798809 |
| precision |
0.557143 |
0.798809 |
| recall |
1 |
1.02425e-22 |
| mcc |
0.0623 |
0.798809 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
1.28771 |
nan |
| auc |
0.495056 |
nan |
| f1 |
0.251613 |
0.798809 |
| accuracy |
0.536 |
0.798809 |
| precision |
0.557143 |
0.798809 |
| recall |
0.1625 |
0.798809 |
| mcc |
0.0623 |
0.798809 |
Confusion matrix (at threshold=0.798809)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
229 |
31 |
| Labeled as 1 |
201 |
39 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.692348 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.648649 |
0.4314 |
| accuracy |
0.48 |
0.4314 |
| precision |
0.48 |
0.4314 |
| recall |
1 |
0.4314 |
| mcc |
0 |
0.4314 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692348 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.648649 |
0.4314 |
| accuracy |
0.48 |
0.4314 |
| precision |
0.48 |
0.4314 |
| recall |
1 |
0.4314 |
| mcc |
0 |
0.4314 |
Confusion matrix (at threshold=0.4314)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
260 |
| Labeled as 1 |
0 |
240 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 2_DecisionTree |
1 |
| 4_Default_Xgboost |
1 |
| 6_Default_RandomForest |
2 |
Metric details
|
score |
threshold |
| logloss |
0.693207 |
nan |
| auc |
0.559287 |
nan |
| f1 |
0.648649 |
0.247797 |
| accuracy |
0.572 |
0.491957 |
| precision |
0.571429 |
0.491957 |
| recall |
1 |
0.247797 |
| mcc |
0.138482 |
0.490173 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.693207 |
nan |
| auc |
0.559287 |
nan |
| f1 |
0.492891 |
0.491957 |
| accuracy |
0.572 |
0.491957 |
| precision |
0.571429 |
0.491957 |
| recall |
0.433333 |
0.491957 |
| mcc |
0.138446 |
0.491957 |
Confusion matrix (at threshold=0.491957)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
182 |
78 |
| Labeled as 1 |
136 |
104 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
6.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.717369 |
nan |
| auc |
0.503766 |
nan |
| f1 |
0.648649 |
0.072 |
| accuracy |
0.522 |
0.465873 |
| precision |
0.506849 |
0.465873 |
| recall |
1 |
0.072 |
| mcc |
0.0222207 |
0.465873 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.717369 |
nan |
| auc |
0.503766 |
nan |
| f1 |
0.236422 |
0.465873 |
| accuracy |
0.522 |
0.465873 |
| precision |
0.506849 |
0.465873 |
| recall |
0.154167 |
0.465873 |
| mcc |
0.0222207 |
0.465873 |
Confusion matrix (at threshold=0.465873)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
224 |
36 |
| Labeled as 1 |
203 |
37 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
5.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.692599 |
nan |
| auc |
0.554559 |
nan |
| f1 |
0.648649 |
0.246802 |
| accuracy |
0.564 |
0.49588 |
| precision |
0.562874 |
0.499565 |
| recall |
1 |
0.246802 |
| mcc |
0.121917 |
0.49588 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692599 |
nan |
| auc |
0.554559 |
nan |
| f1 |
0.488263 |
0.49588 |
| accuracy |
0.564 |
0.49588 |
| precision |
0.55914 |
0.49588 |
| recall |
0.433333 |
0.49588 |
| mcc |
0.121917 |
0.49588 |
Confusion matrix (at threshold=0.49588)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
178 |
82 |
| Labeled as 1 |
136 |
104 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
56.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.693393 |
nan |
| auc |
0.504215 |
nan |
| f1 |
0.648649 |
0.424219 |
| accuracy |
0.522 |
0.496033 |
| precision |
0.502203 |
0.496033 |
| recall |
1 |
0.424219 |
| mcc |
0.0405242 |
0.496033 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.693393 |
nan |
| auc |
0.504215 |
nan |
| f1 |
0.488223 |
0.496033 |
| accuracy |
0.522 |
0.496033 |
| precision |
0.502203 |
0.496033 |
| recall |
0.475 |
0.496033 |
| mcc |
0.0405242 |
0.496033 |
Confusion matrix (at threshold=0.496033)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
147 |
113 |
| Labeled as 1 |
126 |
114 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
1.6 seconds
Metric details
|
score |
threshold |
| logloss |
0.712674 |
nan |
| auc |
0.523886 |
nan |
| f1 |
0.648649 |
0.132938 |
| accuracy |
0.55 |
0.532795 |
| precision |
0.557252 |
0.532795 |
| recall |
1 |
0.132938 |
| mcc |
0.0921318 |
0.532795 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.712674 |
nan |
| auc |
0.523886 |
nan |
| f1 |
0.393531 |
0.532795 |
| accuracy |
0.55 |
0.532795 |
| precision |
0.557252 |
0.532795 |
| recall |
0.304167 |
0.532795 |
| mcc |
0.0921318 |
0.532795 |
Confusion matrix (at threshold=0.532795)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
202 |
58 |
| Labeled as 1 |
167 |
73 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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